A cost effective framework for analyzing cross-platform software energy efficiency

In recent history, most desktop software has been built for x86-based CPUs and developers rarely needed to consider cross-platform development. With ARM emerging as a promising architecture for both high performance and low power, we have reached a landmark for multi-architecture development. The most notable example at present is the introduction of Apple silicon (ARM-based CPU) and Apple’s substantial efforts to migrate software from previously supported Intel CPUs to ARM CPUs. Porting software requires significant work and it is difficult for developers to predict the performance and energy efficiency of software early on without testing it on the target hardware. Privileged access to prototype hardware and limited options for simulation before release further complicate the problem. In this paper, we propose a cost effective framework that allows developers to estimate the energy consumption of their software on a new platform without modifying their code and without direct access to target hardware. Specifically, our framework includes three modules: (1) The instruction prediction module uses a machine learning approach to automatically generate code for a target platform. (2) The energy estimation module leverages portable energy scores to calculate the energy consumption of this generated code. (3) The instruction profiling module analyzes the cross-platform code in an efficient way and produces evaluation reports. Our experiments conducted on the CoreMark® and LINPACK benchmarks have shown encouraging results in terms of program correctness and estimated energy consumption when porting software from x86 CPUs to ARM CPUs.